1. Field of the Invention
This invention relates to data recognition systems and more particularly to a computer system and method for estimating and signaling the recognition accuracy of inputs in a data recognition system.
2. Description of the Background Art
In order to increase convenience and ease of use of their products, the manufacturers of computer systems have expanded the input devices of computer systems to accept inputs in such data forms as audio signals and handwriting data. Speech recognition systems, for example, accept an audio input and analyze it to determine the command or data. The, systems determine the appropriate action to take by analyzing the input in the frequency and time domains and comparing the analyzed input to expected inputs. The systems generate a list of statements that possibly match the input. Each possible matching statement, also known as a theory, has a matching score that indicates how closely, as determined by the system, the theory matches the input. Generally, the systems recognize the theory with the highest matching score as the matching statement. The theory with the highest matching score, however, does not necessarily, actually match the input.
This approach to recognizing data signals has several problems. It bases the selection of the matching data simply on the magnitude of the matching scores of the theories. The theory with the highest matching score does not necessarily truly represent the content of the input. The probability that the system will recognize the wrong theory is particularly high, when the difference in matching scores between the highest theory and the next highest theory is very small.
A prior art solution to this problem is to set a threshold for the difference between the matching score of the highest scoring theory, which is recognized as the correct match, and the next highest scoring theory. This difference is referred to as the confidence score of the highest scoring theory. The confidence score indicates the confusability of the theory having the highest matching score with the other generated theories. For inputs that have a high degree of confusability, the confidence score will generally be low; whereas utterances that are not confusable will generally have a high confidence score. If the confidence score is above the threshold, the system will recognize the highest scoring theory as the match. If the confidence is below the threshold, the system will not recognize the highest scoring theory as the match. In this latter case, the prior art system will simply ignore the input.
This prior art solution has several problems. The approach is Boolean; the system either recognizes the input or it does not recognize the input. There is no provision for a measure of the probability that the highest scoring theory does not match the input. Any threshold is arbitrary and depends on the designer of the system. Thus, the threshold may be too broad or too narrow. The threshold also will not account for systematic signal variations such as accents and dialects as might be encountered within a language. In the case of speech recognition systems, assuming the correct threshold is identical for all dialects and accents within a language, that threshold may not be correct for other languages.
There is a need in data recognition systems for a system and method for accurately determining and signaling the probability that the recognized data matches the input. The system and method should be based upon the expected language of the input and must be sufficiently fast so that it can operate in real time with the input device.